Frank Wolfe Policy Optimization

Frank-Wolfe Policy Optimization is a reinforcement learning technique used to solve constrained optimization problems in policy learning, particularly where actions are limited by constraints such as resource budgets or physical limitations. Current research focuses on improving sample efficiency and convergence properties, often employing neural network architectures and dual-critic designs to handle competing objectives like minimizing distortion while adhering to rate constraints. This approach finds applications in various fields, including video compression and network control, offering potential for improved performance and efficiency compared to traditional methods.